bio-imaging-mass-cytometry-spatial-analysis
Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-spatial-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: anndata 0.10+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scipy 1.12+, squidpy 1.3+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Spatial Analysis for IMC **"Analyze spatial cell interactions in my IMC data"** → Build spatial neighborhood graphs, test for cell-cell interaction enrichment, and identify spatial domains from multiplexed imaging data. - Python: `squidpy.gr.spatial_neighbors()`, `squidpy.gr.nhood_enrichment()` ## Build Spatial Graph ```python import squidpy as sq import anndata as ad # Load phenotyped data adata = ad.read_h5ad('imc_phenotyped.h5ad') # Ensure spatial coordinates are set # adata.obsm['spatial'] should contain (x, y) coordinates # Build spatial neighbor graph sq.gr.spatial_neighbors(adata, coord_type='generic', delaunay=True) # Or by distance sq.gr.spatial_neighbors(adata, coord_type='generic', radius=50) # 50 pixel
- Version Compatibility
- Build Spatial Graph
- Neighborhood Enrichment
- Co-occurrence Analysis
- Ripley's Statistics
- Cell-Cell Interaction
- Custom Neighborhood Analysis
- Spatial Clustering
- Interaction Hotspots
- Visualize Spatial Patterns
- Statistical Testing
- Export Results
- Related Skills
What does the bio-imaging-mass-cytometry-spatial-analysis skill do?
Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-spatial-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.